AIMC Topic: Neoplasms

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Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.

Nature reviews. Clinical oncology
In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks m...

Machine learning for tissue diagnostics in oncology: brave new world.

British journal of cancer
Machine learning is an exciting technology with broad application in big data analysis, as well as increasingly in specialised healthcare. As a diagnostic tool in tissue workup and pathology, it has the potential for personalised and stratified appro...

Cross-registry neural domain adaptation to extract mutational test results from pathology reports.

Journal of biomedical informatics
OBJECTIVE: We study the performance of machine learning (ML) methods, including neural networks (NNs), to extract mutational test results from pathology reports collected by cancer registries. Given the lack of hand-labeled datasets for mutational te...

Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining.

Pharmacology & therapeutics
A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into e...

Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BMC bioinformatics
BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic...

A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images.

Medical & biological engineering & computing
This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict n...

Wx: a neural network-based feature selection algorithm for transcriptomic data.

Scientific reports
Next-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. ...

Engineering approaches for characterizing soft tissue mechanical properties: A review.

Clinical biomechanics (Bristol, Avon)
From cancer diagnosis to detailed characterization of arterial wall biomechanics, the elastic property of tissues is widely studied as an early sign of disease onset. The fibrous structural features of tissues are a direct measure of its health and f...

Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks.

Mathematical biosciences
A kind of noncoding RNA with length more than 200 nucleotides named long noncoding RNA (lncRNA) has gained considerable attention in recent decades. Many studies have confirmed that human genome contains many thousands of lncRNAs. LncRNAs play signif...

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine.

Advanced materials (Deerfield Beach, Fla.)
Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patie...